Mobile animal surveillance and distress monitoring

ABSTRACT

A method for remote animal surveillance and distress monitoring includes detecting biometric and behavioral parameters of the animal, identifying novel events based on comparison of detected parameters to predefined parameter values and qualifications; determining whether a composite parameter value exceeds a predefined composite threshold value indicative of possible distress in the animal; and notifying remote caretakers of possible distress in the animal based on the composite value exceeding the predefined composite threshold value.

RELATED APPLICATIONS

This application claims priority to and the benefit of the filing dateof U.S. provisional application Ser. No. 61/943,677 filed on Feb. 24,2014 entitled “System and Method for Mobile Animal Surveillance andDistress Monitoring”, and 62/087,076 filed on Dec. 3, 2014 entitled“Method for Mobile Animal Surveillance and Distress Monitoring” whichare incorporated herein in their entireties by reference.

BACKGROUND OF THE INVENTION

This disclosure relates to detection of animal distress, andparticularly to notification of caregivers of such distress. Excludingold age, colic is the leading cause of death in domesticated horsesregardless of breed, sex, and usage. It is estimated that this conditionafflicts nearly 5% of horses in the US each year and more than 11% ofthese cases will be fatal. Casting (i.e., stall casting), althoughseldom traumatic, is another highly-common cause of serious injury tohorses. Unfortunately, colic and casting often occur overnight or atremote locations when/where caretakers are not present, resulting indelayed intervention. Delayed intervention is a negative prognosticindicator that has dire impact on survival and quality of life outcomes.Another important time for caregivers to be present is when a mare(i.e., female horse) is about to foal (i.e., give birth). This processoccurs very quickly and while more than 90% or mares foal normally, aminority percentage can experience complications that require humanintervention to reduce the risk of injury or death to the foal and/ormare.

Colic

Colic is one of the most dangerous and costly equine medical problems.Colic is a symptom of disease, but not a disease itself, and isgenerally defined as any abdominal pain. Equine colic can involve anynumber of abdominal organs, not just the gastrointestinal tract. Forexample, abdominal discomfort from kidney or liver disease willsometimes cause signs of colic. Equine colic can originate from thestomach, small intestine, large intestine, or some combination thereof,and is associated with any malfunction, displacement, twisting,swelling, infection, or lesion of any part of the equine digestivesystem.

Equine colic is multifaceted and its diagnosis can be elusive withsymptoms ranging from subjective and subtle changes in the animal'sattitude (e.g., depression) to objective changes in the animal's vitalsigns (e.g., increased heart and respiratory rates, rise intemperature), biologic functions (e.g., lack of digestion), andactions/movements (e.g., pawing, kicking, flank watching,rising/falling, rolling+/−thrashing). Further, a horse in distress willnot usually display a healthy shake upon rising/standing after rollingor lying down. A horse suffering from colic may show any number of thefollowing signs:

-   -   Pawing and/or scraping (front legs)    -   Kicking (back legs) up, or at abdomen    -   Repeated lying down and rising/standing    -   Rolling (+/−thrashing)    -   Stretching    -   Pacing    -   Flank watching (i.e., turning of the head to watch stomach        and/or hind quarters)    -   Biting/nipping the stomach    -   Repeated flehmen response (i.e., curling of upper lip)    -   Groaning    -   Bruxism (i.e., excessive grinding of the teeth or clenching of        the jaw)    -   Excess salivation    -   Loss of appetite    -   Change in attitude; depression    -   Frequent attempts to urinate    -   Lack of normal digestive/gut noise    -   Lack of defecation    -   Increased heart rate    -   Increased respiratory rate    -   Increased temperature    -   Sweating

The causes of colic are not absolute and may include, but are notlimited to:

-   -   Obstruction of the gastrointestinal tract from food or other        materials    -   Impaction of food material in the gastrointestinal tract    -   Buildup of gas inside of the abdomen    -   Parasitic infestation by roundworms, tapeworms, cyathostomes,        and/or strongyles    -   Dorsal displacement    -   Torsion of the gastrointestinal tract    -   Intussusceptions    -   Epiploic foramen entrapment    -   Strangulating lipoma    -   Mesenteric rent entrapment    -   Gastric ulceration    -   Enteritis    -   Colitis

While many animals can suffer from colic, horses—especially those thatare stabled—are particularly susceptible due to a multitude of factors,including heavily grain-based diets, relatively small stomach volume,the inability to release excess gas by eructation, susceptibility toparasitic infestation, and a highly convoluted gastrointestinal tract.Treatment for equine colic varies depending on the cause and severity ofthe condition ranging from rest and medication to invasive emergencysurgery. Different types of colic include, but are not limited to:

-   -   Stomach distention: The small capacity of a horse's stomach        makes it susceptible to distension when excessive amounts of        food are ingested. When a horse gorges itself on grain, or a        substance which expands when dampened like dried beet pulp, the        contents of the stomach can swell. Unlike humans, horses have a        valve at the distal end of their esophagus into the stomach that        only opens only one way, and as a result horses cannot        regurgitate. If something is eaten to disrupt their digestives        system there is only one direction digesta can travel. The        horse's small stomach and their inability to regurgitate may        result in distension and potential rupture of the stomach.    -   Displacement: The small intestine is suspended in the abdominal        cavity by the mesentery and is free floating in the gut. In a        displacement, a portion of the intestine has moved to an        abnormal position in the abdomen. This mobility can predispose        the small intestine to become twisted. Except in rare cases, the        result is total blockage of the intestine requiring immediate        surgery. During twisted intestine surgery, the intestine is        repositioned and any portion of the intestine that is damaged        due to restricted blood flow is removed. Displacement colic can        be caused by gas build up in the gut that makes the intestines        buoyant and subject to movement within the abdominal cavity.    -   Impaction colic: Impaction colic occurs when the intestine        becomes blocked by a food mass that's too large to easily pass.        The large intestine folds upon itself and has several changes of        direction (flexures) and diameter changes. These flexures and        diameter shifts can be sites for impactions, where a firm mass        of feed or other foreign material blocks the intestine.        Impactions can be induced by coarse feed stuff, dehydration, or        accumulation of foreign material.    -   Gas colic: Most cases of colic are associated with some gas        build up. Gas can accumulate in the stomach as well as the        intestines. As gas builds up, the gut distends, causing        abdominal pain. Excessive gas can be produced by bacteria in the        gut after ingestion of large amounts of grain or moldy feeds.        The symptoms of gas colic are usually highly painful but        non-life threatening unless untreated, and then displacement        becomes a possibility.    -   Spasmodic colic: This occurs due to increased contractions of        the smooth muscle in the intestines. These intestinal        contractions, or abnormal spasms, cause the intestines to        contract painfully. Over-excitement or over-stress of the animal        can trigger spasmodic colic.    -   Sand colic: When fed on the ground in sandy regions sand can        accumulate in the horse's cecum. The irritation can cause        discomfort, and if there are significant amounts of sand        present, the weight can cause the cecum to become displaced.    -   Enteritis/colitis: In some cases, abdominal pain is due to        inflammation of the small intestine (enteritis) or large        intestine (colitis). These conditions are the result of        inflammation of the intestine, and may be caused by bacteria,        grain overload, or tainted feed. Horses with enteritis/colitis        may also have diarrhea. Enteritis and colitis are often hard to        diagnose and may present themselves similar to displacement or        impaction colic.    -   Parasite infections: Certain types of parasitic infections can        cause colic. Strongyles, a type of parasitic worm, cause        intestinal damage that can restrict blood flow to the intestine.        Damage to the walls of the intestine produce a roughened surface        that can accumulate clots. Other colic producing parasites in        horses include ascarids (roundworms) and bot flies which can        cause stomach blockage resulting in colic.    -   Stress: Travel, herd changes, schedule disruptions, and other        traumatic events can contribute to stress in an animal which may        result in colic.

Casting

Stall casting occurs when a horse lies down or rolls in a stall and getstrapped too closely to the wall. When this occurs the horse is not ableto gain sufficient leverage and stand up. Subsequently, the horse maybecome frightened and begin thrashing, likely resulting in injury.Exhaustion to the point of shock is another concern with a distressedhorse that is cast.

Nearly all cases of casting require human intervention to assist theanimal to turn over. If the horse is relatively quiet, 2 persons may beable to reposition the horse by pulling it over gently by the tail orhind legs (with the aid of a lunge line), while simultaneously pullingthe horse's head over. If the horse is too panicked, sedation may berequired before any attempt is made to reposition and turn over thehorse.

Foaling

Giving birth to a foal occurs over 3 stages. The ability for a caretakerto recognize each of these stages is critical to assess whetherintervention is needed. However, the ability to have live humanmonitoring and evaluation 24 hours a day during the last few weeks of a340-day gestation period is challenging for many.

Stage 1: Positioning of the Foal

-   -   During this stage (1-4 hours) the fetus gradually shifts from a        position on its back and rotates until its heads and forelimbs        are extended in the birth canal. Over several hours the pregnant        mare may appear restless and become very nervous. She will        likely have several transient periods of pacing, walking the        fence line, and colic-like symptoms (e.g., pawing, kicking,        rising/falling+/−healthy shake, rolling+/−thrashing). Mares in        the pasture will also move away from other horses and towards        complete isolation.

Stage 2: Delivery of the Foal

-   -   During this stage (15-20 minutes) the fetus moves down the birth        canal, the mare's water breaks, and the foal is born. Due to        very strong contractions of the abdominal and uterine wall        muscles, the mare usually lies on her side (i.e., on her flanks)        with her legs fully extended although she may also rise/fall        several times to reposition the foal, sometimes with the foal's        head and limbs protruding. During this stage it's important for        the caretaker to check the positon of the foal within the        vagina; lower the foal to the ground if the mare is standing;        reposition the mare away from any wall, fence, or other        obstacle; and break open the amniotic sack and untangle the        umbilical cord, if required.

Stage 3: Expulsion of the Placenta

-   -   During this final stage (1-8 hours), the placenta is expulsed.        If the placenta has not been expulsed after 3 hours, the        caretaker should alert a veterinarian. It is also important for        the caretaker to tie-up the afterbirth in a knot such that it        hangs over the mare's hocks during this period.        Problems with Current Technology

Although there are a few technologies (i.e., equine foaling/birthingmonitors) on the market today, all these products have seriousshortcomings. Their cumbersome design, rudimentary analytical methods,and limitations in wireless transmissions prevent them from being usedreliability on a large scale as foaling/birthing monitors, let alonesecondary use to assist in detecting colic, casting, or other distressstates of animals.

Belly Bands: In practice, horses tend to become preoccupied with nippingat belly bands, making it a distraction for horses and staff. The bellyband also introduces a new injury risk due to the transmitter unitmounted on the horse's back. A horse experiencing severe colic is likelyto roll frequently and often. As such, a horse wearing the unit on itsback is likely to roll onto the unit, which may result in a back injury.The methods for mounting this, which is similar to other foaling sensorson the market today, and the positioning of these sensors on a horsemake them suboptimal for the detection of colic.

Behavior Analysis: Most foaling/birthing monitors rely solely on motionsensors to assess whether an animal is lying down or on its side for aspecific period of time, which is likely to be plagued with manyfalse-positive findings.

Radio Frequency Transmission: A few systems use simple radio frequency(RF) transmitters to signal an alarm when triggered. These systems canbe connected to a phone line or pager to automatically alert caretakers.These systems use single-channel RF modulation to transmit sensor data.Because multiple transmitters interfere with each other and theirsurroundings (e.g., metal barns), the RF approach cannot be scaled-upfor large operations with many horses or be used while in transit.Interference can also arise from other RF transmitters, such as cordlessphones or other similar devices located nearby.

Given the deficiencies of the technologies cited above, barn managersresort to (if anything) round-the-clock night checks by caretakersand/or night watchmen to monitor the health and safety of their horses.Such laborious checks by humans are time consuming, subjective, costly,and not without error. Even with individuals on location twenty-fourhours a day in a veterinary facility or barn, signs of distress ortrouble might not be caught as early as desired. Accordingly,improvements are sought in the detection of animal distress andnotification of caregivers. The present invention remedies many of theseproblems and limitations.

SUMMARY OF THE INVENTION

While the way that the present invention addresses the disadvantages ofthe prior art will be discussed in greater detail below, in general, thepresent invention provides a mobile adaptive sensor and notificationsystem (“MASNS”) for surveillance of animals, and more particularly forthe analysis of biometrics (e.g., vital signs), biologic functions(e.g., digestion), and behaviors (e.g., posture, motion patterns) thatmay indicate a variety of problematic health conditions, some of whichmay result in serious injury or death of the animal. The vital signs ofan animal, coupled with its biologic functions, posture, andactions/movements can directly correlate with a physiological state andbehaviors that are indicative of distress (e.g., colic), trauma (e.g.,casting), other conditions where human intervention is warranted (e.g.,foaling).

There are over 9.2 million horses estimated to be in the United States(>58 million worldwide), with more than 40% of these animals being keptfor recreational purposes, nearly another 40% being kept for performancecompetitions (i.e., racing and showing), and the balance being kept forfarm, ranch, and police work, as well as use in rodeos, polo matches,and as carriage horses. When a horse is transported or used heavily forperformance competitions their stress levels increase and subsequentlytheir chance for developing colic or becoming cast tends to be morefrequent. Colic and casting are especially serious issues withhigh-value horses, which are more likely to be transported forperformance competitions and breeding.

When a horse is experiencing colic and/or is cast, the animal will be ina distressed state as evidenced by measurable changes in biometrics(e.g., vital signs) and biologic functions (e.g., digestion), posture(e.g., lying down), and repeated characteristic motion patterns (e.g.,pawing, kicking, rising/falling+/−healthy shake, rolling+/−thrashing).When this occurs, human intervention is needed to assess the severity ofdistress and establish a care plan. Mild cases of colic may be resolvedby simply hand-walking a horse for 15 minutes, whereas severe cases ofcolic may require invasive emergency surgery. A cast horse may sometimesreposition themselves to stand-up independently, but more often humanintervention is needed to assist the animal. A mare who is about to foalwill also have measurable changes in her vital signs and awell-characterized set of recurrent actions and movements.Identification of these changes in biometrics and behaviors will signalthe start of stage 1, and therefore an important time for the caretakerto be present to monitor and address any complications that may arise.Regardless of whether a horse is experiencing colic, is cast, or ishaving foaling complications their outcome is directly correlated withtime to intervention. Delay of intervention is a negative prognosticindicator that has dire impact on outcomes, including permanent injuryand even death.

Early detection of animal distress, such as colic in horses, may lead toprompt treatment that can vastly improve outcomes and increase theanimal's chances of survival. Due to the high cost of colic surgery andpoor survival outcomes with untreated colic, it is especially desirablefor the animal to receive medical treatment at the first signs of colic.Thus, a reliable mobile animal surveillance and distress monitoringsystem in the form of a wearable MASNS noninvasively attached to ananimal that can relay a notification to caretakers—when signs ofdistress and other serious conditions that require immediateintervention are identified—is useful in safeguarding horses without theneed for humans to be present for round-the-clock monitoring.

The MASNS and method of its use disclosed herein comprises a multiplexset of sensors for measuring biometrics, monitoring biologic functions,evaluating posture and motion patterns, assessing environmental factors,and determining the exact location of an animal; a computationalprocessor for real-time analysis of all sensor inputs to identify,differentiate, and validate specific states and behaviors of an animal;and a wireless transceiver for bidirectional communications to transmitnotifications to a caretaker, receive user queries, and update thesystem's software and firmware. The system can be configured to monitorthe physiological state, biologic functions, behavioral patterns, andlocation of a wide variety of animals including, but not limited to,horses, cattle, elk, llamas, bison, bears, sheep, deer, companionanimals (i.e., dogs, cats), etc.

One application for the MASNS is the broad surveillance of horses todetect novel events. Novel events are described as those biometrics,biologic functions, and/or behavioral activity that are outside thedefined parameters and limits of the system. Another application is thecontinuous monitoring of horses where biometrics, biologic functions,and/or behavioral activity is analyzed within the predefined parametersand predefined limits of the system. The system detects changes inbiometrics and biologic functions compared with both defined parametersand limits (for training of the model) and adaptively-derived thereafterto each animal's unique historical and empirical values/thresholds. Thesystem also evaluates posture and actions/movements compared to eachanimal's historical “normal” behavior and characteristic motion patternsthat may be indicative of colic, casting, foaling, or other seriousconditions that require immediate intervention. Various MASNSembodiments may contain any combination of an ultrawide band-impulseradar (“UWB-IR”), a thermal infrared sensor (“TIRS”), a microphone, a3-axis accelerometer, a 3-axis gyroscope, a 3-axis magnetometer, asingle-axis barometric pressure sensor, an optical light sensor, and alocation sensor (e.g., GPS, Wi-Fi or cellular triangulation).

The UWB-IR, TIRS, and microphone outputs correlate with the generalphysiologic state of the animal, and are used as a first-level filter toidentify a possible distress state. The accelerometer, gyroscope,magnetometer, barometric pressure sensor, and location sensor outputscorrelate with coarse posture, position, and motion information, and areused to classify behavior as “normal” vs. “non-normal” as well asqualify the type of actions and motions (e.g., pawing, kicking,rising/falling+/−healthy shake, rolling+/−thrashing) Finally, theoptical light sensor correlates with the environmental conditions of theanimal (e.g., inside/artificial light vs. outside/natural light).

The MASNS analyzes all biometric, biologic function, behavioral, andenvironmental inputs, to determine the presence and relative degree ofdistress using a fuzzy logic-based model. In this model multiple inputsare evaluated to derive a single quantitative output measure of relativedistress (i.e., an Equine Distress Index [“EDI”]), ultimately informingthe system whether or not to issue one or more wireless multi-levelnotifications (e.g., “watch” vs. “warning” vs. “alert”). If the sensorunit detects biometrics and/or biologic functions outside acceptablelimits for an unusual period of time along with characteristic posturesand/or motion patterns that are outside normal limits for an unusualperiod of time, algorithms compare the data with predefined parametersand historical value/thresholds for each individual animal to determineif a distress situation is occurring and to remotely/wirelessly triggera notification via a communication protocol. When notification istriggered or when the system is queried, the outputs of the GPS unitand/or triangulation via Wi-Fi or cellular signal strength correlatewith the latitudinal and longitudinal coordinates of the animal wearingthe MASNS device and can be used to precisely locate the distressedanimal.

The device may be implemented to continuously monitor horses in avariety of locations including, but not limited to, stalls, pastures,breeding centers, show barns, and veterinary clinics, as well as intrailers, trucks, vans, and/or other modes of transportation. Whendistress is detected, the device may relay the emergency situation toappropriate caretakers via wireless communication methods in a cascadingor escalating fashion.

Various research applications are also enabled by the monitoring, suchas identifying more subtle conditions based on biometrics, biologicfunctions, and behavioral signatures of wild horse herds. Macro analysisof historical data for larger aggregate populations and smaller cohortsmay also lead to the discovery of new risk factors and/or markers ofearly onset colic and/or other conditions. The opportunity to performpredictive analytics on the system's data may also prove beneficial topolicy makers, insurance providers, and others interested in protectingthe welfare or horses and their owners.

One aspect of the invention features, in some embodiments/applications,a method for remote animal surveillance and distress monitoring. Themethod includes detecting one or more biometric parameter of the animal;detecting one or more behavioral parameter of the animal; determiningoccurrence of a novel event based on comparison of detected parametersto a range of predefined parameter values and qualifications; computinga composite value for a combination of detected parameters; determiningwhether the composite value exceeds a predefined composite thresholdvalue indicative of possible distress in the animal; and notifying oneor more remote caretakers of possible distress in the animal based onthe composite value exceeding the predefined composite threshold value.

In some embodiments/applications, determining occurrence of a novelevent includes determining when one or more of the detected parametersfall outside one or more of predefined parameters or historicalparameters for the animal.

In some embodiments/applications, determining occurrence of a novelevent comprises use of a one-class classifier.

In some embodiments/applications, the method includes detecting one ormore biologic function parameter of the animal and using detectedbiologic function parameters for at least one of determining theoccurrence of a novel event and computing the composite value.

In some embodiments/applications, the method includes updating the rangeof predefined parameter values and qualifications, and compositethreshold values in an on-going fashion to conform to detectedparameters for the animal over time.

In some embodiments/applications, the method includes use of fuzzy logicto derive the composite value.

In some embodiments/applications, notifying one or more caretakersincludes activation of an escalating notification protocol acrossmultiple channels.

In some embodiments/applications, the one or more biometric parameterincludes one or more of a respiratory rate, heart rate, and temperatureof the animal.

In some embodiments/applications, detecting the one or more behavioralparameters includes monitoring data from one or more of anaccelerometer, gyroscope, magnetometer, and barometric pressure sensor.

Another aspect of the invention features, in someembodiments/applications, a method for detecting one or more biometricparameter in animals. The method includes using UWB-IR to acquire one ormore of respiratory rhythm data and cardiac rhythm data; differentiatingbetween the respiratory rhythm data and the cardiac rhythm data byfiltering and principal component analysis followed by independentcomponent analysis for feature reduction and extraction throughconditioning of acquired rhythm data; using fast Fourier transform forfrequency analysis of the conditioned rhythm data to determine a powerlevel of respective dominant frequencies; and correlating the respectivedominant frequencies with a respiratory signal and a cardiac signal ofthe animal.

In some embodiments/applications, the method includes determining whenone or more of a respiratory rate and a cardiac rate of the animalexceeds one or more of a predefined threshold or historical thresholdindicative of possible distress in the animal.

In some embodiments/applications, the method includes notifying one ormore remote caretakers of the possible distress in the animal based onthe determining.

Another aspect of the invention features, in someembodiments/applications, a method for mobile equine surveillance anddistress monitoring. The method includes monitoring at least one of therespiratory rate and the heart rate of an animal using UWB-IR;monitoring the temperature of an animal using a thermal infrared sensor;monitoring the behavior of the animal using at least one of anaccelerometer, gyroscope, magnetometer, and barometric pressure sensor;determining the posture and location of the animal using at least one ofa barometric pressure sensor, global positioning system sensor, andWi-Fi triangulation; determining when at least one parameter of therespiratory rate, heart rate, temperature, behavior, and posture of theanimal meets a single threshold value; determining when a combination ofparameters of the respiratory rate, heart rate, temperature, behavior,and posture of the animal meets a composite threshold value indicativeof possible distress in the animal; and activating of an escalatingnotification protocol across multiple channels to inform one or moreremote caretakers of possible distress in the animal.

In some embodiments/applications, the method includes detecting one ormore biologic function parameter of the animal.

In some embodiments/applications, the method includes monitoring ofdigestive activity of the animal using a microphone.

In some embodiments/applications, the method includes detection of oneor more novel events though use of a one-class classifier when one ormore detected biologic function parameter falls outside one or more ofpredefined parameters or historical parameters for the animal; andupdating a range of predefined biologic function parameter values andqualifications, single threshold values, and composite threshold valuesin an on-going fashion to conform to detected parameters for the animalover time.

In some embodiments/applications, the method includes determiningoccurrence of one or more novel events though use of a one-classclassifier when one or more detected parameters fall outside one or morepredefined parameters and historical parameters for the animal;

In some embodiments/applications, the method includes updating a rangeof predefined parameter values and qualifications, single thresholdvalues, and composite threshold values in an on-going fashion to conformto detected parameters for the animal over time.

In some embodiments/applications, the method includes sending anotification when one or more of the heart rate, respiratory rate, andtemperature is outside an adaptively-derived empirical upper limit ofnormal and lower limit of normal for the animal while at rest.

In some embodiments/applications, the method includes generating one ormore of a first watch notification when the heart rate is greater thanabout 15% above the resting normal (RN) or is greater than about 15%below the RN for a period of time, a second warning notification whenthe heart rate is greater than about 40% above the RN or is greater thanabout 40% below RN for a period of time, and a third alert notificationwhen the heart rate is greater than about 70% above the RN or is greaterthan about 70% below RN for a period of time. The method furtherincludes generating one or more of the first watch notification when therespiratory rate is greater than about 35% above the RN or is greaterthan about 35% below RN for a period of time, the second warningnotification when the respiratory rate is greater than about 75% abovethe RN or is greater than about 50% below the RN for a period of time,and the third alert notification when the respiratory rate is greaterthan about 150% above the RN or is greater than about 65% below the RNfor a period of time. The method further includes generating one or moreof the first watch notification when the temperature is greater thanabout 1% above the RN or is greater than about 1% below the RN for aperiod of time, the second warning notification when the temperature isgreater than about 2% above the RN or is greater than about 1.75% belowthe RN for a period of time, and the third alert notification when thetemperature is greater than about 4% above the RN or is greater thanabout 3.5% below the RN for a period of time.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present invention may be derived byreferring to the detailed description and claims when considered inconnection with the Figures, wherein like reference numerals refer tosimilar elements throughout the Figures. Understand that Figures depictonly certain embodiments of the invention and are therefore not to beconsidered limiting of its scope. Embodiments will be described andexplained with additional specificity and detail through the use of theaccompanying Figures.

FIG. 1 illustrates one embodiment of Mobile Animal Surveillance andDistress Monitoring.

FIG. 2 illustrates one embodiment of a MASNS module.

FIG. 3 illustrates one embodiment of a MASNS decision-making protocol.

FIG. 4A illustrates one embodiment of a MASNS decision matrix: Watchnotification.

FIG. 4B illustrates one embodiment of a MASNS decision matrix: Warningnotification.

FIG. 4C illustrates one embodiment of a MASNS decision matrix: Alertnotification.

FIG. 5 illustrates one embodiment of a hierarchy of FISs for overalldistress.

FIG. 6 illustrates an example of membership functions within a fuzzysystem.

FIG. 7 illustrates an example of Mamdani and Larsen composition andimplication operators.

FIG. 8 is a graphical representation of input membership functions andshape for heart rate.

FIG. 9 is a graphical representation of input membership functions andshape for respiratory rate.

FIG. 10 is a graphical representation of input membership functions andshape for temperature.

FIG. 11 is a graphical representation of output memberships for Watch,Warning, and Alert notifications.

FIG. 12 is a graphical representation of FIS implementation usingexample fuzzy rules and database for heart rate.

FIG. 13 is a graphical representation of FIS implementation usingexample fuzzy rules and database for respiratory rate.

FIG. 14 is a graphical representation of FIS implementation usingexample fuzzy rules and database for temperature.

FIG. 15 is a graphical representation of biometric risk using examplefuzzy rule aggregation and defuzzification.

FIG. 16A illustrates one embodiment of a behavior algorithm.

FIG. 16B illustrates one embodiment of a behavior classifier for aspecific target behavior.

FIG. 17A illustrates one embodiment of NED with one-classclassification.

FIG. 17B illustrates one embodiment of NED data window collection.

FIG. 17C illustrates one embodiment of NED model learning.

FIG. 18 illustrates one embodiment of a biometric algorithm.

FIG. 19 illustrates one embodiment of a respiratory rate algorithm.

FIG. 20 illustrates one embodiment of a heart rate algorithm.

FIG. 21A illustrates one embodiment of a respiratory rate algorithm withone reading from a UWB-IR device.

FIG. 21B illustrates one embodiment of a respiratory rate algorithm with100 samples from a UWB-IR device.

FIG. 21C illustrates one embodiment of rhythm data after filtering andzero mean.

FIG. 21D illustrates one embodiment of PCA components of rhythm data.

FIG. 21E illustrates one embodiment of ICA components of rhythm data.

FIG. 21F illustrates one embodiment of smoothened ICA components ofrhythm data.

FIG. 22A illustrates one embodiment of 2D FFT analysis of rhythm datausing all ICA components.

FIG. 22B illustrates one embodiment of 1D FFT of individual frequenciesof ICA components.

FIG. 22C illustrates one embodiment of FFT of rhythm data afterSNR-based ICA component removal.

FIG. 22D illustrates one embodiment of FFT of rhythm data after summingFFT coefficients of ICA components.

FIG. 23 illustrates one embodiment of summed FFT of ICA components afterfiltering of respiratory rate.

FIG. 24 illustrates one embodiment of real-time respiratory and heartrates for a horse over 7.5 minutes.

FIG. 25 illustrates one embodiment of linearized real-time respiratoryand heart rates for a horse over 7.5 minutes.

FIG. 26 illustrates one embodiment of a biologic algorithm.

DETAILED DESCRIPTION OF SELECTED EMBODIMENTS

The following description is of exemplary embodiments of the inventiononly, and is not intended to limit the scope, applicability orconfiguration of the invention. Rather, the following description isintended to provide a convenient illustration for implementing variousembodiments of the invention. As will become apparent, various changesmay be made in the function and arrangement of the elements described inthese embodiments without departing from the scope of the invention asset forth herein. It should be appreciated that the description hereinmay be adapted to be employed with alternatively configured deviceshaving different shapes, components, sensors, mechanisms and the likeand still fall within the scope of the present invention. Thus, thedetailed description herein is presented for purposes of illustrationonly and not of limitation.

Reference in the specification to “one embodiment” or “an embodiment” isintended to indicate that a particular feature, structure, orcharacteristic described in connection with the embodiment is includedin at least an embodiment of the invention. The appearances of thephrase “in one embodiment” or “an embodiment” in various places in thespecification are not necessarily all referring to the same embodiment.

In the following description, numerous specific details are provided fora thorough understanding of specific embodiments. However, those skilledin the art will recognize that embodiments can be practiced without oneor more of the specific details, or with other methods, components,materials, etc. In some cases, well-known structures, materials, oroperations are not shown or described in detail in order to avoidobscuring aspects of the embodiments. Furthermore, the describedfeatures, structures, or characteristics maybe combined in any suitablemanner in a variety of alternative embodiments. Thus, the following moredetailed description of the embodiments of the present invention, asrepresented in the drawings, is not intended to limit the scope of theinvention, but is merely representative of the various embodiments ofthe invention.

Disclosed are embodiments of mobile animal surveillance and distressmonitoring systems, in the form of a wearable MASNS that analyzesreal-time biometrics, biologic functions, behaviors, and environmentalconditions associated with the health and safety of animals, as well ascoordinates to track location of animals. The MASNS includes a multiplexof sensors, a power source, a processing unit, a wireless transceiver,data analysis functions, one-class classifiers, algorithms,bi-directional communication protocols, and a means for associating thesystem with an animal for long-term mobile surveillance (e.g., wearablesmart technology apparatus in the form of a harness and/or clothing).The embodiments described herein are presented within the context ofequines, but it should be obvious to one skilled in the art the MASNS isapplicable to a host of different animals under a myriad of conditions.Equine health issues, such as colic, casting, and foaling, are indicatedby changes in a horse's biometrics, biologic functions, posture, and keycharacteristic motion patterns. The MASNS detects such indicativebiometric changes, biologic functions, and behavioral patterns bymonitoring the horse's physiologic state, posture, andactions/movements.

System Overview

With reference to FIG. 1, the method for remote animal surveillance anddistress monitoring comprises 3 phases: acquisition, analysis, andnotification. During the acquisition phase the MASNS device iscontinuously obtaining data on an animal at home, at a breeding center,at a show barn, at a vet clinic or other establishment regardless ofwhether in a stall or pasture, or while in transit on a trailer, truck,or van. During the analysis phase, the device determines the location,general state, and well-being of the animal by processing and evaluatingreal-time biometrics, biologic functions, behaviors, and environmentalconditions at the point-of-care (i.e., at the level of the animal). Ifthe system determines that the animal is experiencing distress duringthis analysis phase, the MASNS will proceed to the notification phaseand send a wireless signal to a central computing station where apredefined cascading communication protocol will be executed to notifythe animal's caretaker(s) of their distress state and location forintervention. At any point the caretaker(s) or other authorized user canremotely query the MASNS device and receive, via a visual dashboard froma computer, tablet, or smart phone, real-time and historical metrics ondata acquired.

Physical Design

One or more MASNS devices are associated/affixed to an animal withinsmall water-tight and dust-resistant enclosure(s) containing sensors andelectronic components remotely mounted on an animal via asmart-technology apparatus (e.g., harness, clothes) to monitor itsbiometrics, biologic functions, behaviors, environmental conditions, andlocation around the clock or at designated intervals without the needfor human supervision or effort.

With continued reference to FIG. 1, in one embodiment, the MASNS deviceis seamlessly integrated within a horse's safety/breakaway halter orcollar. In different embodiments, the MASNS device can be non-invasivelyattached to a facial apparatus (e.g., halter, bridle), neck apparatus(e.g., collar, neck sweat), surcingle, sheet/blanket/hood, or otherhorse tack or equipment as appropriate. For other animals, the MASNSdevice can be attached to the animal using ear tags, harnesses, anklebands, tail mounts, or other appropriate techniques. Further, in anotherembodiment, the MASNS device (whole are in part) may be associated withthe animal in vivo.

In one embodiment, the sensor's enclosure(s) bend to follow the naturalcontour of the horse's head, poll, and neck. In other embodiments thesensor's components fit in a single small enclosure. The small,integrated, water-tight and dust-resistant features of the MASNS devicemakes it suitable for routine long-term use in a wide range of businesssettings and operations. In one embodiment, because the MASNS device isintegrated and contained within a horse's safety/breakaway halter orcollar, the device poses little risk of snagging on fences, feeders, orother objects, nor does it protrude or have an unusual appearance thatmay attract the curiosity of other horses.

With reference to FIG. 2, one embodiment of the MASNS remote unitincludes, but is not limited to multi-axis motion sensor(s), biometricsensor(s), biologic sensor(s), single-axis barometric pressuresensor(s), optical light sensor(s), location/position sensor(s),electronic module(s) with microcontroller(s) and microprocessor(s),battery(s), wireless transceiver(s), and other associatedelectronics/additional components. The individual components can bearranged in the remote unit enclosure(s) in a variety of configurations.The microprocessor is programmed to analyze and control the functions ofthe electronic components in the MASNS device. The multi-axis motionsensor(s), barometric pressure sensor(s), and location/position sensorscan provide coarse posture and location information (e.g., the sensors'tilt angle in multiple dimensions), as well as fine motion information(e.g., pacing, shaking, struggling). The transceiver is the basis forreceiving a signal from a user device, as well as for wirelesscommunication of the distress indicator alarm once activated.

The remote unit's noninvasive design, long battery life, and wirelesscommunication capabilities makes it a safe, convenient, and practicalsolution for routine, long-term monitoring of animal health and safetyand is suitable for adoption in large-scale operations such as breedingcenters, show and racing barns, and veterinary clinics and hospitals.

MASNS Decision-Making Protocol

In order for the MASNS device to determine whether or not to send anotification indicating the animal is in distress, a systematic protocolis followed. With reference to FIG. 3, one embodiment of the MASNSdecision-making protocol includes three parallel detection paths withtheir respective sensor suites (i.e., biometric sensors, biologicsensors, motion sensors) and one additional path for inputs from othersensors (e.g., barometric pressure sensors, location/position sensors,optical light sensors). Each sensor suite feed respective detectionalgorithms (i.e., biometric algorithm, biologic algorithm, behavioralgorithm, and novel-event detection [“NED”] algorithm). Biometricinformation can include, e.g., heart rate, respiratory rate, bodytemperature, etc. Biologic information can include, e.g., digestive/gutsounds, groaning sounds, bowel movements, abdominal distension,perspiration, etc. Behavioral information can include, e.g., rise, fall,roll, lie down, shake, flank watch, paw, kick, spin, flehmen response,bruxism, windsuck, crib, weave, etc. as well as new and novel actionsand movements considered unique when compared to that animal'shistorical behavior.

The NED algorithm determines whether or not the equine is in a “normal”or “novel event” state based on motion sensor(s) and its trainedclassifier. If a novel event is not detected, the animal is behavingnormal and the MASNS does not need to generate a notification. If anovel event is detected, then the window of the novel event is sent tobehavior algorithm for further evaluation. The behavior algorithmdetermines whether the novel event is one of the target behaviors knownto serve as a surrogate marker of distress or other state that mayrequire human intervention. If the novel event is not one of the targetbehaviors, the MASNS does not need to generate a notification. If thenovel event is one of the target behaviors, then the behavior algorithmsends the target behavior and its parameters to a fuzzy inference system(FIS) for an overall quantitative measure of relative distress or EDI.

Similarly to the behavior algorithm, the biometric and biologicalgorithms detect and prepare the biometric and biologic data of thesame time interval. If any of the biometric or biologic algorithm outputvalues are within normal ranges, the MASNS does not generate anotification. If any of the biometric or biologic data are out of normalranges, then they are sent to the FIS for further evaluation and anoverall quantitative measure of relative distress or EDI.

With reference to FIGS. 4A-C, in some embodiments, the MASNSdecision-making protocol uses decision matrix criteria as the basis forcreating the fuzzy rule base and shape of fuzzy membership functions ofthe FIS as shown in FIG. 5 and FIG. 6. The outputs of the biometricalgorithm, biologic algorithm, behavior algorithm, and inputs from othersensors all feed into the FIS. In the FIS, data from algorithms andinputs from other sensors are evaluated and a multi-level notificationin the form of “Watch,” “Warning,” or “Alert” is generated.

Fuzzy Logic

Fuzzy systems make use of input variables that are represented as fuzzysets as opposed to crisp values. These fuzzy sets are used to attempt toquantify some uncertainty, imprecision, ambiguity, or vagueness that maybe associated with a variable. Commonly, these fuzzy systems are definedby using if-then rules. A FIS is an application of fuzzy logic that canbe utilized to help online decisions processes. A rule-based fuzzysystem is typically realized as a set of sub-systems including aFuzzifier, Fuzzy Database, Fuzzy Rule Base, Fuzzy Inference, and aDefuzzifier as shown in FIG. 6.

Fuzzification

-   -   is defined as the mapping of a crisp value to a fuzzy set. A        fuzzifier represents the fuzziness of a variable by defining        membership functions. There are three popular fuzzifiers that        are used, singleton, Gaussian, and triangular. With a Gaussian        or triangular fuzzifier some of the uncertainty with a system        variable may be described and can help reduce noise. Singleton        fuzzifiers generally do not provide this noise suppression.

Fuzzy Database

-   -   The database for a rule-based fuzzy system is the set of        linguistic terms and their membership functions. Fuzzy        membership functions are functions that define a mapping of an        input set to its belonging to the fuzzy membership set itself        (membership degree). A membership degree of ‘0’ indicates the        input set does not belong to the fuzzy membership set, whereas a        ‘1’ indicates full membership. There are many different fuzzy        membership functions that can be used such as triangular,        trapezoidal, Gaussian, bell, sigmoidal, and many others. For        each membership function defined for an input space, a        linguistic term is assigned to it; such as HIGH, LOW, AVERAGE,        NEGATIVE, POSITIVE, etc.    -   For an example of a database for a FIS, consider a temperature        sensor. Three general membership functions could be        linguistically defined COLD, WARM, and HOT. From the linguistic        terms it is the designer's choice how these membership functions        are to be shaped (possibly based on empirical evidence).

Fuzzy Rule Base

-   -   For rule-based fuzzy systems, variables and their corresponding        relationships are modeled through the means of if-then rules.        The general form of these if-then rules is:        -   IF antecedent proposition THEN consequent proposition    -   Using a linguistic fuzzy model, as introduced by Mamdani, the        antecedent and consequent are fuzzy propositions. The general        form of a linguistic fuzzy model if-then rule follows as:        -   Ri: If {tilde over (x)} is Ai Then {tilde over (y)} is Bi    -   Where {tilde over (x)} is the input (antecedent) linguistic        variable, and Ai are the antecedent linguistic values of {tilde        over (x)}. The output (consequent) linguistic variable is        represented as {tilde over (y)} with Bi corresponding to the        consequent linguistic values of {tilde over (y)}. The linguistic        terms, Ai, are fuzzy sets that defines the fuzzy region in the        antecedent space for respective consequent propositions. Ai and        Bi are typically predefined sets with terms such as Large,        Small, High, Low, etc. Using these linguistic terms an example        of a linguistic fuzzy model if-then rule could be:        -   If temperature is HIGH Then risk is HIGH    -   Most systems are Multiple-Input and Single-Output (MISO) or        Multiple-Input and Multiple-Output (MIMO). For MISO and MIMO        systems the antecedent and consequent propositions can be a        combination of univariate fuzzy propositions. The propositions        may be combined using common logic operators such as conjunction        or disjunction. The general rule form for a MISO system is        below:        -   Ri: If x1 is Ai,1 and/or x2 is Ai,2 and . . . xp is Aip Then            y is Bi    -   Substituting in some linguistic terms, an example of a MISO rule        would be:        -   If temperature is MED and breathing is HIGH Then risk is            MED-HIGH

Fuzzy Inference

-   -   The inference procedure or compositional rule of inference is        determined by two operators: implication operator and        composition operator. The two most common compositional rules of        inference are Mamdani and Larsen. Each of these have different        operators to implement implication and composition.        -   Mamdani        -   Implication: min operator        -   Composition: max-min        -   Larsen        -   Implication->algebraic product operator        -   Composition->max-product    -   The difference in implementation of the different implications        is shown in FIG. 7.

Defuzzifier

-   -   The output of the FIS is multiple fuzzy sets that correspond to        the degree of influence each rule has on the output. In order to        generate a crisp value for the inference, the rule sets need to        be aggregated and then defuzzified. One of the most common        defuzzification techniques are Center of Gravity (CoG) or        centroid, and the weighted average. The CoG technique is most        accurate but can be computationally expensive, where the        weighted average can provided a good estimate with significantly        less computation.

The overall assessment of distress is determined on the basis of manyfactors within the entire system, including biometric, biologic,behavioral, and preexisting risk factors. Biometric and biologic factorsinclude input from processing algorithms that provide information suchas heart rate, respiratory rate, temperature, and possibly digestiveindicators. Behavioral factors provide information about daily behaviorbased on motion data by estimating behavioral repetition, duration, andtime-based relationships. The preexisting risk factors involvequalitatively assessing predisposal to distress based on environmentalconditions, physical characteristics, and preexisting health issues. Inorder to provide an overall quantitative measure of relative distress orEDI from all these factors a hierarchy of FIS is used. The overallhierarchy is seen in FIG. 5.

With reference to FIG. 5, it is seen that each FIS uses information froma subset of the factors to provide a level of distress for each of therespective subsets of factors. Then each subset's distress level isprovided to final FIS for an overall quantitative measure of relativedistress or EDI, and decide if any of three notification levels arewarranted. These notification levels include “watch, warning,” and“alert” each respectively relating to increasing levels of distress. Anadditional gain stage is used for the biometric and biological inferencesystems to adjust the level of distress based on duration. Theseduration adjustments are to reduce false distress assessments frombiometric and biologic changes that may occur during elevation in normalphysical activity or noise from sensor readings. The actualimplementation of each FIS can be generically described through theprovided case studies detailed below.

Example/Case Study Fuzzy Inference System

This section provides a case study of how the implementation of anindividual FIS is achieved. For this case study, the biometric systeminputs are used as they are best fit for fuzzy logic memberships andlogistic terms. In this section, the use of fuzzifiers are explained,preliminary generation of membership functions/linguistic terms for thedatabase are provided, an example rule base discussed, and a potentialdefuzzification method visualized.

Fuzzification of Biometric Input

-   -   Each of the biometric inputs provides a crisp value for their        estimate of a biometric reading. For the biometric inputs to be        used in a fuzzy inference system, the crisp biometric value        requires fuzzification. As discussed in the introduction, the        most common fuzzifiers are singleton, Gaussian and triangular. A        non-singleton fuzzifier is chosen since the reported biometric        inputs have some uncertainty associated with their estimates.        More specifically, a Gaussian fuzzifier is used because of the        ease of computation and implementation over a triangular        fuzzifier. A Gaussian fuzzifier is shaped per biometric input        such that the Gaussian fuzzifier's variance corresponds to the        uncertainty of the biometric inputs.

Fuzzy Database: Membership Functions and Linguistic Terms

-   -   The input membership functions are chosen to be Gaussian and        sigmoidal for their potential reduction in computation in        comparison to triangular/trapezoidal membership functions. The        actual shape of these member functions are determined by a few        parameters per membership. The parameters themselves are        selected based on criteria provided by the decision matrix shown        in FIGS. 4A-C. FIGS. 8-10 are example input memberships. Actual        shapes of these functions will be determined by either a 1)        statistical norms reported from literature/experts (listed in        decision matrix) based on a broad range of horses and/or 2) by a        statistical study on a per horse basis. Either way, the        statistics will generate parameters to be used to shape the        input of the membership functions.    -   Linguistically, the terms Critically Low (CL), Low (L), Below        Normal (BN), Normal (N), Above Normal (AN), HIGH (H), and        Critically High (CH) have been selected to related to the upper        and lower thresholds for the predetermined three levels of        distress that include Watch, Warning, and Alert. Output        memberships for Watch, Warning, and Alert are created to serve        as linguistic implications for various compositions of the        inputs and membership functions. Examples of the output        membership functions are shown in FIG. 11.

Fuzzy Rule Base

-   -   The fuzzy rule base for the biometric FIS has the potential to        be generated by numerous rules considering there are three        inputs each with seven input membership functions and three        output membership functions. Only a few sample rules are        provided. Three sample rules are provided below using        Respiratory Rate (RR), Heart Rate (HR) and Body Temperature        (Temp) along with the fuzzy database previously discussed.        -   Rule 1: If RR is HIGH Then Risk is WARNING        -   Rule 2: If HR is ABOVE NORMAL Then Risk is WATCH        -   Rule 3: If Temp is ABOVE NORMAL Then Risk is WATCH    -   For these rules, only a single input was used per rule, but let        it be noted that multiple inputs could be used. If multiple        inputs are used then they need to be composed using the        appropriate conjunctions such as shown in the following rules.        -   Rule 4: If RR is HIGH and HR is HIGH then Risk is WARNING        -   Rule 5: If Temp is HIGH or Temp is LOW then Risk is WARNING    -   Inference    -   For a given set of fuzzified biometric inputs, fuzzy rule base,        and fuzzy database; inference for risk is calculated using a        FIS. The output of the FIS is further defuzzified to provide a        crisp assessment of biometric risk. For ease of explanation, the        example fuzzy database and Rules 1-3 will be used to overview        the FIS implementation.    -   There are several FIS design choices, but in hindsight of        computational complexity those with less computation        requirements have been selected. Larsen implication (algebraic        product operator) and composition (max-product) has been        selected due to computational advantages of algebraic product        operator over the max operator. Graphical representation of the        FIS implementation for the example rules can be seen in FIGS.        12-14.    -   With reference to FIG. 12, a graphical representation of the FIS        implementation is illustrated for example rules for detecting        heart rate.    -   With reference to FIG. 13, a graphical representation of the FIS        implementation is illustrated for example rules for detecting        respiratory rate.    -   With reference to FIG. 14, a graphical representation of the FIS        implementation is illustrated for example rules for detecting        temperature.

Defuzzification

-   -   Once all the rules are composed and implied to their        corresponding outputs, the result is fuzzy sets in the form of        Gaussians representative of each rule's influence on the output.        The aggregation of all these rules needs to be defuzzified to        generate a crisp value for biometric risk. For computational        complexity reduction, the weighted averages defuzzification        method is used. FIG. 15 shows the result of the aggregation of        the rules and the final defuzzified output. For the three        example rules and example inputs, the final inference is an        assessment of a biometric risk value of 3.84.

Motion Sensors

A multi-axis sensor is actually a number of sensors combined together. A9-axis sensor includes a 3-axis accelerometer, a 3-axis gyroscope, and a3-axis magnetometer. This 9-axis sensor combines information provided byall of the sub-sensors to generate a dataset that describes in detailthe movements of the monitored animal. A single-axis barometric pressuresensor captures the absolute altitude of the MASNS device and furtherrepresents another input for analysis.

When a horse is experiencing distress there are a number of movementsthey may enact instinctually in response. While different stressors canelicit different movements, the differentiation between these movementsmay also provide information as to the type of stressor that isaffecting the animal. External stressors (e.g., presence of predators)may cause the horse to repeatedly spin in circles and buck, whereasinternal stressors (e.g., abdominal discomfort) may cause the animal torepeatedly lie down/rise and roll with or without thrashing of its legsor presence of a healthy shake upon standing/rising. Thesecharacteristic motion patterns to internal stressors can assist in thediagnosis of certain conditions such as colic. Many of these physicalmovements/actions indicating a potential colic are observable throughthe use of the multi-axis motion sensor coupled with or without othermotion sensors.

Behavior Algorithm

-   -   With reference to FIG. 16A, the behavior algorithm is a        classifier capable of cataloguing data segments using a        classifier trained from expertly classified-data segments.        During online operation of the behavior algorithm, data        segments, which have been previously defined as novel, are        provided to the algorithm. Thus, the behavior algorithm        evaluates the data segments, which are marked as novel events by        the NED algorithm. The NED algorithm determines the start and        the end of the novel event and provides the corresponding data        segment to the behavior algorithms. Each behavior algorithm is        trained to identify a target behavior and outputs the        probability of the novel event being the target behavior. Target        behaviors can be categorized into different levels:        -   Primary: rise, fall, roll, lie down, no healthy shake, flank            watch, paw, kick, etc.        -   Secondary: spin, flehmen response, bruxism, etc.        -   Tertiary: windsuck, crib, weave, etc.    -   As novel events happen rarely during the daily routine of the        animal there will be limited number of samples (i.e., data        segments). Thus, classifiers (i.e., one-class classifiers) used        in the NED algorithm cannot be directly used, as there will not        be enough statistics to calculate mean and standard deviation of        the samples. However, an expert may select these parameters for        a one-class classifier such that the successful classification        can be done. Another possibility is to use classifiers where        limited data can be accommodated such as Radial Basis Function        Networks (“RBFN”) and neural networks. RBFN are feed-forward        neural networks where a layer of N basis functions (commonly        Gaussian functions). The weighted sum of the outputs of the        basis functions is the output(s) of the RBFNs. For a given data        set and chosen mean and standard deviations for the Gaussian        basis functions, the weights of the output layer can be learned.        When there is enough statistics, the training samples are        clustered and mean and standard deviations of the clusters are        assigned to be the parameters of the Gaussian basis functions.        As the novel events will be rare, RBFN with fixed parameters        will be more suitable to learn a generalized model for a        specific target behavior. However, with sufficient statistics,        the parameters of the RBFNs can be calculated from the training        samples. FIG. 16B presents the flow diagram for a behavior        classifier for a specific target behavior.

Novel-Event Detection (NED)

-   -   NED provides the capability of identifying and classifying novel        data segments, or windows, contained in a series of        semi-continual data samples. A novel window is one that has any        new or unknown information that was not used or was not        available during algorithmic training Each window is composed of        samples of motion sensor data. During training, a model is        created to represent the sensor data during normal conditions.        For this application, normal conditions are defined as periods        of activity without motion behaviors that may be indicative of        distress. Thus, the model created during the training process is        referred to as the normal model. During online operation,        windows of motion data are sequentially provided to the NED        algorithm. Each window is compared against the trained normal        model and classified. Those windows that are rejected by the        normal model are classified as novel and their data and time        information become candidates for further analysis. Contiguous        novel windows are then grouped together and defined as a novel        event. A novel event is capable of providing indicators for        stress-related behaviors that cannot be contained within a        single window.

NED Algorithm

-   -   With reference to FIG. 17A, the NED process is continually        running while the system is in an active data collection mode.        This process begins by obtaining the most current window of raw        motion data from the Data Window Collection process        (implementing any preselected algorithmic or model parameters).        After a window of data is obtained, it is preprocessed to        convert the window into a feature vector. This feature vector is        further reduced in size and used in a one-class classifier. The        one-class classifier compares the feature vector to a normal        model and provides a binary decision of “normal” or        “non-normal.” Non-normal indicates that the current feature        vector is rejected by the normal model. Once a window is        determined as normal/non-normal its novelty is estimated based        on previous windows' classifications. If the window is        determined as novel its data and time information are stored.    -   Each of the NED procedures is listed below with additional        high-level details.        -   1. Data Window Collection            -   This process involves the windowing of the raw motion                data from the sensors. The output of the Data Window                Collection process is the most recent window of raw                motion data.        -   2. Preprocessing            -   After collecting the most current window of raw motion                data, this window is converted into a format more                suitable for classification. During preprocessing, the                time-based motion is filtered to remove noise,                transformed into the frequency domain, and the power of                individual frequency bands computed. These powers of                frequency bands are used to generate the feature vector.                -   i. Filter—To filter high frequency noise from the                    filter a low-pass Butterworth filter is used.                -   ii. Frequency Transformation—Each sensor's data                    within the window is transformed using the Fast                    Fourier Transform (“FFT”) to obtain coefficients                    relative to frequency components of each sensor's                    raw data.                -   iii. Band Power—The absolute value of each sensor's                    FFT coefficients is used to represent the power of                    the individual frequencies. Neighboring frequencies'                    powers are combined to determine the power within                    bands of frequencies.                -   iv. Feature Vector Creation—The feature vector is                    created by concatenating the frequency power bands                    from all the sensors into a single vector.        -   3. Feature Reduction            -   The feature vector is further reduced based on feature                reduction parameters that were learned during the Model                Learning process.        -   4. One-Class Classification            -   For one-class classification, the reduced feature vector                is input into the normal model that was generated during                the Model Learning process. The model itself is a                Gaussian Mixture Model (“GMM”) that was learned to                represent sensor data under normal conditions. The                output of the mixture model is a probability that the                input vector belongs to the model also referred to as                likelihood. If the likelihood is lower than a set                threshold value the feature vector is rejected from the                model.        -   5. Estimation of Novelty            -   Even though a window may be classified as non-normal, it                may not indicate that the window is a part of a novel                event. The current window's novelty is estimated using                previous windows' normal/non-normal classifications.                This is done to help reduce the number of false                positives that the system may produce.

NED Data Window Collection

-   -   With reference to FIG. 17B, the process of windowing the raw        motion data collected from the sensors is described. The flow is        essentially the realization of sliding window data collection.        The system is continually sampling data from the motion sensors        and the purpose of the data window collection process is to        buffer and shift the sampled data in preparation to be provided        to other processes such as the NED algorithm or Model Learning        process. The Data Window Collection process is continually        running while the system is actively collecting data.

NED Model Learning

-   -   With reference to FIG. 17C, the model learning procedure is one        that is preformed offline from the actual system itself. The        purpose of the model learning process is to generate a model        that represents sensor data during normal conditions. It has        been previously stated that normal is defined as periods of        activity without motion behaviors that may be indicative of        distress. During the learning process data is collected from        stored field data; stored data allows for offline processing.        The learning process uses the same data collection and        preprocessing techniques as seen in FIG. 17A.    -   The overall offline procedure for model learning begins by        collecting stored data samples from known normal conditions. The        raw data samples are prepossessed to generate feature vectors to        be used in model creation. The feature vectors are then used to        learn a Principal Component Matrix (“PCM”) to be used for future        feature reduction. The features are reduced using the learned        PCM. The reduced features are split into training and validation        subsets, where the training data is used to train the model and        validation used to validate the model. Using the training        feature vectors, a GMM is learned using the Expectation        Maximization (“EM”) algorithm. The learned model is applied to        the validation feature vectors and the fitness of the model is        compared to that of the training fitness. If the fitness values        are similar than the model learning process is complete        otherwise it must be repeated with different data.    -   Each of the procedures is listed below with additional        high-level details.        -   1. Data Collection            -   Same process discussed in NED algorithm. The only                difference for data collection during model learning is                that only normal data is used. Therefore, any window of                raw data that contains a previously known event is not                included in the dataset for learning.        -   2. Preprocess            -   Same process discussed in NED algorithm.        -   3. Learn Feature Reduction            -   The feature reduction process uses Principal Component                Analysis (“PCA”), which is a method of projecting data                in to a smaller principal component space. The specific                PCA method done was that as defined by Alpaydin (Ethem                Alpaydin. Introduction to Machine Learning. The MIT                Press, Cambridge, Mass., second edition, 2010). PCA                during the model learning process is applied to all the                data windows selected for learning and these learning                windows are only from data segments know to be from                normal conditions. After applying PCA to the learning                set, a PCM is determined. This PCM may be used to reduce                the dimensionality of the feature vector to contain a                smaller subset of features that are statistically                significant enough to explain the learning dataset.        -   4. Perform Feature Reduction            -   The learned PCM is used to reduce all of the feature                vectors for learning.        -   5. Split Data            -   The reduced learning feature vectors are split into two                groups. One group is for training the model and the                other is used to validate the model. This process is                very common and its purpose is to check for                over-training of the model and essentially robustness of                the model.        -   6. Learn Model            -   The model used is a GMM, which is a probabilistic model                and is used to represent the sensor data under normal                conditions. The actual derivation and implementation of                a GMM is in accordance with McLachlan and Peel (Geoffrey                McLachlan and David Peel. Finite mixture models. John                Wiley & Sons, 2004). To learn the model parameters the                EM algorithm is used. EM is a commonly used method to                estimate model parameters for a mixture model,                especially targeting Gaussian mixtures. The specific                implementation of EM used is one published by Verbeek et                al (J J Verbeek, N Vlassis, and B Krose. Efficient                greedy learning of gaussian mixture models. Neural                computation, 15(2):469-85, February 2003).            -   During the learning process, the preprocessed and                reduced training feature vectors are used in the EM                algorithm. EM learns the GMM parameters including means,                covariances and weights. The number of mixture                components is preselected based on empirical trials.                After the model is learned, the training feature vectors                are input into the model to get their likelihood of                belonging to the learned model. One-class classification                is applied to likelihoods to get a quantitative result                of the fitness of the model.        -   7. Validate Model            -   To validate the model, the validation data is applied to                the learned model, likelihood values obtained, and                one-class classification performed. The result of the                one-class classification from the validation data is                compared to the result from the training data. If these                results are reasonably close then the model training is                complete. In the event that the training and validation                results are not close, the whole model process will need                to be repeated using a better training set of data.        -   The aforementioned procedure can be reapplied on a per            animal basis at any given time and repeated infinitely to            adapt and configure the system for each specific animal            (i.e., data from a robust set of incidences on an individual            animal vs. robust data from a sample population of multiple            representative animals).

Biometric and Biologic Sensors

The MASNS contains biometric and biologic sensors capable of monitoringphysiological parameters of a horse, including but not limited to heartrate, respiratory rate, temperature, and digestive sounds. Whenencountering a stress (e.g., colic, being cast, foaling) a horse willhave certain physiological responses such as the release of adrenaline,which gets their body ready for a fight-or-flight response. Thisfight-or-flight response can be seen in all mammals and evidenced by anincrease in heart rate and blood pressure so they can be best preparedto respond to the stress-inducing stimulus. A horse's heart rate (i.e.,pulse), along with other vital signs (i.e., respiratory rate and bodytemperature) and biologic functions (i.e., digestive sounds), serve assurrogates for a horse's overall physiological state, and thereforerepresent useful targets for monitoring distress in horses.

The system in this disclosure is able to monitor known physiologicresponses to stress through the use of biometric and biologic sensors.The horse's pulse (normal range of about 30-40 beats per minute) ismonitored through the use of an UWB-IR and a TIRS; the horse'srespiratory rate (normal range of about 8-16 breaths per minute) ismonitored through the use of an UWB-IR and a microphone; the horse'sbody temperature (normal range of about 98.6°-100.4° Fahrenheit;slightly higher in foals and warm weather) is monitored through the useof an TIRS; and the horse's digestive sounds (normal characteristicsounds are rumbling and gurgling no less than every 10-20 seconds vs.sloshing or inaudible/faint sounds lasting more than about 1 minute) ismonitored through the use a microphone.

The MASNS constantly monitors these vital signs and biologic functionsin the animal, and runs the real-time data through algorithms todetermine if there is sufficient indication of distress in the animal towarrant alerting the animal's caretaker(s). If, after the MASNS hasprocessed these physiologic and other data inputs, the system hasdetermined that there is sufficient evidence that the animal isexperiencing an abnormal amount of distress, it will trigger anotification.

It is important to note that, in horses, some of the physiologicresponses to stress can be mirrored by normal responses to situationswhen the animal is not in a distressed state. For example, a horses'heart and respiratory rates will increase when the horse is simplyrunning. As such, the biometric data being processed by the MASNScomprises one of many parameters that the system analyzes in order todetermine whether or not the animal is in a stressed state or not.

Biometric Algorithm

-   -   With reference to FIG. 18, the biometric algorithm is a        collection of signal processing algorithms for determining        biometrics of an animal. Biometrics are values that describe        specific anatomical condition/rate of the horse such as heart        rate, respiratory rate, and temperature. Thus, any biometric        gathered from an animal presents information regarding the        health of the equine and can be analyzed by a veterinarian or a        system.    -   Sensors can provide data in two ways (1) a sensor that provides        the biometric value directly, such as TIRS; (2) a sensor that        provides raw data for a specific biometric value to be        calculated, such as UWB-IR. In the case of a sensor that        provides the biometric value directly, only signal conditioning        and signal processing is needed. In the case of a sensor        providing raw data, there is a need for a detection algorithm        for each biometric value. In FIG. 18, respiratory rate detection        algorithm and heart rate detection algorithm are specifically        designed to calculate respective rates. The final stage of the        biometric algorithm is biometric data preparation where these        values are prepared to be integrated into the MASNS        decision-making protocol as depicted in FIG. 3        -   Respiratory Rate and Heart Rate Algorithms        -   The respiratory and heart rates are determined by analyzing            data provided by an UWB-IR. Respiratory rate and heart rate            algorithm flow charts are illustrated in FIG. 19 and FIG. 20            respectively. The UWB-IR provides times of flight of radio            frequency signal for a specific range. FIG. 21A shows one            reading of an UWB-IR sensor. The sensor scans 1-meter range            for 6.6 ns. FIG. 21B shows 100 samples in time of such            readings as an illustration as a new sample is provided in            approximately 250 ms. FIG. 21C presents the result of an            initial signal processing of the UWB-IR readings (“rhythm            data”) using a “high-pass” filter and removal of the mean in            the sample scale. By removing the mean in the sample scale,            the motion in the object can be seen for a given distance.        -   After removing the mean and cleaning the rhythm data, PCA is            applied to the rhythm data in order to determine the            principle (i.e., important) components of the data. By            keeping most of the information in the data, PCA maps the            rhythm data in a smaller space, in FIG. 21D, this is given            as eight components. Thus, a reduction of the number of            variables in data is from 256 to 8. PCA generates orthogonal            principle components that have similar variance. Thus, the            PCA keeps important information and removes the noise            component from the signal in a compact form.        -   After the PCA, an Independent Component Analysis (“ICA”) is            performed in order to determine independent components of            the signal so that the same information is not repeated in            the signal. FIG. 21E shows eight ICA components calculated            from the PCA components calculated in the previous step.            After applying ICA, the rhythm data is represented in a            compact form where the variables have independent and            important information. FIG. 21F shows the ICA components            after applying a moving average in order to remove high            frequency components and smoothen the ICA components for a            better frequency analysis.        -   After applying signal processing techniques for noise            removal and signal conditioning (FIG. 21C) and initial            signal processing in order to extract important features of            the rhythm data (PCA and ICA: FIGS. 21D-F), the frequency            analysis is done in order to determine the dominant            frequencies, which will represent frequencies related to            respiratory and heart rates. FIG. 22A presents            two-dimensional FFT analysis results of all eight ICA            components. The rhythm data presented is collected from a            horse for about 7.5 minutes. Thus, the respiratory rate            region and heart rate region are shown in FIG. 22A around 6            beats per minute (“BPM”) and 30 BPM. The frequency scale in            the figures is converted to BPM for better visual            presentation. Similarly, FIG. 22B illustrates the frequency            analysis of individual ICA components using one-dimensional            FFT. Respiratory and heart rate regions are marked in all            Figures.        -   The ICA analysis provides independent components of a signal            in a compact form. However, depending on the number of            independent components, the ICA components may have similar            information and/or assign noise elements to one or more ICA            components. Thus, by analyzing the ICA components with            respect to noise content (i.e., signal to noise ratio            [“SNR”]) can reveal the ICA components that have more noise            than signal. Thus, by removing the ICA component that has            the lowest SNR, the number of ICA components can be reduced            in order to have a more compact and relevant variable space            for frequency analysis. We determine the SNR values of each            ICA component by analyzing FFT of each ICA component. The            ICA removal process can be repeated if there are ICA            components that have very low SNR values. FIG. 22C            represents frequency analysis of seven ICA components after            removing the ICA component that has the lowest SNR value. As            can be seen, the respiratory and heart rate regions became            more visible after the removal process. As can be seen in            FIG. 22C, all seven ICA components do not have the same            frequency characteristics. Thus, it is not possible to            determine the respiratory and heart rates from one or more            of the ICA components. However, as the respiratory and heart            rates are dominant signals in the rhythm data, we see            high-power values around their frequencies. Thus, we sum            power of each frequency over all ICA components and expect            to see very high power values around respiratory and heart            rate regions. FIG. 22D shows the resulting frequency            analysis after summing the FFT coefficients of ICA            components over each frequency. By finding the frequency            that has the highest power the respiratory rate can be            determined as marked in FIG. 22C.        -   After determining the respiratory rate (i.e., frequency), a            high-pass filter is applied so that frequencies around the            respiratory rate are removed from the ICA components. FIG.            23 illustrates the frequency powers of the signal after            filtering out the respiratory rate. Then, the heart rate is            calculated by finding the frequency that has the highest            power as depicted by the arrow in FIG. 23.        -   Real-Time Analysis/Determination        -   As the UWB-IR provides a new reading every ˜250 ms, the            heart and respiratory rate algorithms can be applied to a            window of a certain length (i.e., one minute). Then, the            window can be shifted (i.e., 4 seconds) and the rates can be            calculated again for that window. Thus, close to real-time            analysis/determination of respiratory and heart rates is            possible. FIG. 24 shows the real-time measurements of            respiratory and heart rates for a horse for a duration of            7.5 minutes. As can be seen in FIG. 24, the rates can have            some noise as the horse moves the equipment. Thus, a            smoothing is needed to have a more stable reading of            respiratory and heart rates for a horse, as shown in FIG.            25.

Biologic Algorithm

-   -   With reference to FIG. 26, the biologic algorithm is a        collection of signal detection and processing algorithms for        determining biological data that is not a standard biometric.        Biologics are values that describe non-specific anatomical        condition/rate of an animal such as digestive/gut sounds. Thus,        the main difference between biometric algorithm and biologic        algorithm is that biometric algorithm is for standard biological        metrics (biometric) that can be directly assessed.    -   Biologic sensors can be any sensors that are designed to provide        data from an animal such as sound and perspiration (i.e.,        humidity of the skin) These sensors are not limited to sound and        humidity; they can be expanded to collect other biological data        as needed. For example in FIG. 26, a microphone is shown that        senses the digestive/gut sounds and provides raw data for        digestive sound detection and processing in or to qualify the        digestive/gut sounds. This outputs the sound levels in decibels        and their durations. Similarly, biologic detection and        processing is need for each possible biologic sensor in the        system. All the obtained biologic rate/condition feed the        biologic data preparation block where these values are prepared        to be integrated into the MASNS decision making-protocol as        depicted in FIG. 3.

Adaptive Modeling

All animals are different. Horses themselves can differ physiologicallydue to a multitude of factors including breed, sex, age, diet, andactivity level. This scope of differences makes it very difficult toestablish an ideal model for the prototypical healthy horse that is notexperiencing undue distress. Accordingly, it is important to establish aprogram for the system being claimed that can be configured to theparticular individual animal being monitored, instead of simply beingconfigured for the proto-horse. By customizing the interpretation of thedata being acquired to a single individual animal, the device can moreprecisely determine the state of that the animal, and thus moreefficiently achieve its purpose. By tailoring the interpretation of databeing gathered from a particular animal to that particular animal'stendencies, the device is able to minimize the possibility of falsepositives and increase the likelihood of true positives.

The MASNS maintains a historical record of past sensor data for eachindividual animal, which—after a specified period of time—can be fedback into the data analysis system in order to tailor acceptable limitsof the various data parameters being monitored. The MASNS may achievethis adaptation and conformity by manually or automatically updating theacceptable limits of various data parameters being monitored to takeinto account the historical record of past sensor data. In such anembodiment—after a specified period of time—the historical record ofpast sensor data will be assumed to be representative of the animal'snon-stressed state unless otherwise indicated by a user.

Location/Position Sensors

Colic, along with other dangerous equine conditions, requires immediateattention when suspected. Time to intervention for diagnosis andtreatment has a direct impact on that animal's outcomes. Often horsesare located within large pastures, which can be very dark at night, andtheir exact location at any given time is unknown. Further many horsesare transported for performance competitions, often hundreds of milesfrom home on commercial carriers, and their whereabouts is approximateat best to the animals' trainers, owners, and caretakers. Both scenarioscan prove dangerous because when a horse is experiencing stress fromcolic or other conditions, it is of the upmost importance that they betreated as soon as possible.

Not only does this MASNS device assist in the early detection of colic,but the device also has an integrated location/positioning system alongwith the use of Wi-Fi and/or cellular signal strength triangulation topinpoint the exact location of the distressed animal wearing the deviceso that treatment may be administered as soon as possible. Once thedevice has registered a positive state—indicating that the animalwearing the device may be in a distressed state—it activates theintegrated location/positioning systems and transmits real-time dataregarding the exact location/position of the animal in question to thecaretaker via a wireless network. By assisting in rapid detection andtreatment of the animal's condition, the MASNS device is able to providethe animal with the greatest chance of recovery and survival.

Power Management

Power management of the MASNS is critical for long-term use and lowmaintenance operation of the system. The remote MASNS device may remainactive for a set period of time and then shut itself off. In oneembodiment, the device may use small, high-capacity, high density, lowself-discharge rechargeable batteries, such as, or similar to,lithium-polymer (“LiPo”) batteries. These batteries allow the device tosit idle for hours, days, or even months without losing significantbattery charge. A fixture/cradle capable of near-field inductioncharging may be utilized for replenishing power to batteries of MASNSdevice. Alternatively, or additionally, a direct connection comprised ofelectrically-conductive contacts may be utilized for recharging ofbatteries. In another embodiment, the device may use a renewable energyharvesting system (e.g., solar power, thermal energy, wind energy,kinetic energy) as a source of power.

Wireless data transmission can be carefully managed to conserve power.Algorithms in the processing unit may be used to associate vital signs,biologic functions, and animal posture and actions/motions with specificbehaviors of interest. With course analysis being performed by thealgorithms at the point-of-care (i.e., at the level of the animal) andrefined analysis, where warranted, is performed by off-site centralcomputer/station, power and energy is conserved by eliminating the needto transmit all input data from sensors for analysis. Rather, throughpoint-of-care analysis, transmission of data occurs only when certainstates or actions, such as possible distress behaviors, are detected.

Point-of-Care Analysis

The system being claimed is constantly monitoring the animal that iswearing the MASNS device in order to provide the most thorough andaccurate determination of the animal's condition at any given point intime. To be able to do this, the device requires a power source. Whileoperating all of the sensors integrated into the device takes somepower, one of the activities of the system that consumes a large amountof power is the transmission of data to an external source. Due to thehigh power cost of external data transmission, the device may have thedata processing unit integrated into the device itself. If the dataprocessing unit is contained within the device itself the need toregularly transmit large quantities of data to an external source foranalysis is removed. Accordingly, in an embodiment having integrateddata analysis unit, the device would only need to transmit informationto an external source when actively alerting the caretaker of a positivereading of distress or when actively queried by an outside source. Byintegrating the data processing unit into the device itself and nothaving it in an external off-site system, the device can minimize theamount of time and data that must be transmitted externally, thusminimizing power consumption and extending the single charge operatinglife of the system.

Additionally, integrating the data analysis hardware into the deviceitself allows for the data analysis means to be dedicated to theinterpretation of data from just the one animal that the particulardevice is monitoring. If an external off-site data analysis means isbeing used, it is likely not dedicated to monitoring a single animal,but rather aggregate monitoring a multitude of animals simultaneously.Furthermore, coupling the system's data analysis means with adaptivealgorithms, and then limiting the data acquisition and analysis to anindividual animal allows for the customization of variable thresholdvalues for a particular animal under surveillance by a particular MASNSdevice. This results in the system functioning more accurately andefficiently over time.

The processing unit may be configured to have a sleep mode and awake-on-signal operation. In one embodiment the processing unit may bein sleep mode most of the time, requiring little power. The processingunit may then respond to any predetermined parameters that areprogrammed into it by waking and beginning operation when thepredetermined parameters are met. This sleep/wake loop may be, but isnot limited to being, event or time driven. In one embodiment, theinstant-wake time stamp is compared with the previous time stamp fromthe last sleep; if the time difference is not within a designated timeperiod, the time stamp is set to the current time, the sensors aredeactivated, and the sensor unit is put back into sleep mode. This powermanagement loop can essentially be a coarse false-alarm check.

Each physiologic value and characteristic behavior, evaluatedindependently or together, may be an indicator or a counter-indicator ofa distress condition. Positive equine biometric distress indicators mayinclude an elevation of heart rate >40 beats per minute, increase inrespiratory rates >16 breaths per minute, and/or rising of the horse'score body temperatures >100.4 degrees Fahrenheit. Counter equinebiometric distress indicators may include an oscillating heart rate of30-40 beats per minute, respiratory rates of 8-16 breaths per minute,and/or core body temperatures of 98.6-100.4 degrees Fahrenheit.

Positive equine motion distress indicators may include repeated episodesof rising/falling with high activity over an extended time period whilethe horse is lying down (i.e., rolling+/−thrashing of legs), nipping atsides, etc. Counter equine distress indicators may include a full-body“healthy shake” upon standing/rising after rolling and minimal activitywhile the horse is lying down.

Data Transmission Networks

Horses and other farm-type animals are often kept and allowed to roam onlarge tracts of rural land. On such expansive tracts, it is unlikelythat there is the infrastructure present for wireless network coverage.

In one embodiment, the MASNS device incorporates transceivers that arecompatible with use on a wireless network. Alternatively, oradditionally, in other embodiments the MASNS device incorporatestransceivers that operate on other mobile wireless (electromagnetic)systems including, but not limited to 3G networks, 4G networks, Wi-Finetworks (standard and long-range networks), mesh networks, and otherwireless data transmission systems.

The use of transceivers compatible with these different wirelessnetworks may give the device the ability to transmit and receivetransmissions from a broad range of devices over a potentially broaderarea of land coverage than what standard Wi-Fi can offer. Whenenvironmental conditions or the accessibility or cost to connect with acellular network is of concern a base station may be utilized. This basestation will allow multiple MASNS devices to access a single internetconnection provided by user/facility. This is of particular importancegiven the rural, remote, and undeveloped nature of locations where manyhorses and other animals tend to be located.

Bidirectional Communications and Interactions

In one embodiment, the MASNS may contain not only a data transmitter forsending the caretaker alerts when the device determines the animal beingmonitored may be experiencing sufficient stress (so as to requireassistance), but may also contain a wireless receiver. Incorporating awireless receiver into the system allows for bidirectional interaction,which facilitates the exchange of data between the MASNS device andexternal sources. Not only would the system be able to push alerts tothe caretaker, but the caretaker would be able to actively query theMASNS for any number of reasons. The user could send a signal to thereceiver incorporated into the MASNS triggering the system to respondwith the current status of the monitored animal, including real-timereadouts of any/all of the data being collected.

The incorporation of a wireless receiver into the MASNS would not onlyallow the caretaker to remotely access information the system isgathering in real-time, but may also allow for the caretaker to check onthe operational status of the MASNS itself from a remote location. Thisfeature would save the caretaker time, energy, and resources byabolishing the process of tracking down the animal under surveillanceand physically inspecting the MASNS in order to determine itsoperational status. Such operational status and other MASNS calibrationtechniques can be enhanced by multi-sensory indicators/actuators (e.g.,LED lights, vibrators, buzzers). In another embodiment suchindicators/actuators can be incorporated and utilized for Pavlovianconditioning, negative feedback, and blocking.

Data Display

In one embodiment, the information (including real-time data) gatheredby the MASNS can be streamed, or otherwise transmitted to, and displayedon, a remote device. At any time the user may query the MASNS throughthe wireless network. Once queried, the MASNS can transmit records ofthe data parameters monitored by the MASNS to user's remote device,including but not limited to, a computer, a tablet, and a smart phone.This feature allows a user to conveniently check on the status of anyanimal being monitored in a real-time fashion from a remote location,without the need for specialized hardware.

Additionally, this feature will work synergistically with both the useof data transmission through mobile networks and with the aforementionedlocation/positioning system(s) included in the device. By allowing theinformation gathered to be in a format that can be displayed on devicesthat already utilize mobile wireless networks there will be no need forthe user to buy specialized hardware in order to remotely monitor theanimals. Furthermore, by allowing the caretaker to use a portabledevice, such as a smart phone, to link with the location/positioningfunction included in the device, said caretaker may easily receiveupdates with the real-time location of the animal being monitored whilethe caretaker is on the move.

While specific embodiments and applications have been illustrated anddescribed, it is to be understood that the current disclosure is notlimited to the precise configuration and components disclosed herein.Various modifications, changes, and variations apparent to those ofskill in the art may be made in the arrangement, operation, and detailsof the device and methods of the present invention disclosed hereinwithout departing from the spirit, scope, and underlying principles ofthe disclosure.

What is claimed is:
 1. A method for remote animal surveillance anddistress monitoring comprising: detecting one or more biometricparameter of the animal; detecting one or more behavioral parameter ofthe animal; determining occurrence of a novel event based on comparisonof detected parameters to a range of predefined parameter values andqualifications; computing a composite value for a combination ofdetected parameters; determining whether the composite value exceeds apredefined composite threshold value indicative of possible distress inthe animal; and notifying one or more remote caretakers of possibledistress in the animal based on the composite value exceeding thepredefined composite threshold value.
 2. The method of claim 1, whereindetermining occurrence of a novel event comprises determining when oneor more of the detected parameters fall outside one or more ofpredefined parameters and historical parameters for the animal.
 3. Themethod of claim 2, wherein determining occurrence of a novel eventcomprises use of a one-class classifier.
 4. The method of claim 1,further comprising detecting one or more biologic function parameter ofthe animal and using the detected biologic function parameters for atleast one of determining the occurrence of a novel event and computingthe composite value.
 5. The method of claim 1, further comprisingupdating the range of predefined parameter values and qualifications,and composite threshold values in an on-going fashion to conform todetected parameters for the animal over time.
 6. The method of claim 1,further comprising use of fuzzy logic to derive the composite value. 7.The method of claim 1, wherein notifying one or more caretakerscomprises activation of an escalating notification protocol acrossmultiple channels.
 8. The method of claim 1, wherein the one or morebiometric parameter comprises one or more of a respiratory rate, heartrate, and temperature of the animal.
 9. The method of claim 1, whereindetecting the one or more behavioral parameters comprises monitoringdata from one or more of an accelerometer, gyroscope, magnetometer, andbarometric pressure sensor.
 10. A method for detecting one or morebiometric parameter in animals comprising: using ultra-wide band impulseradar (UWB-IR) to acquire one or more of respiratory rhythm data andcardiac rhythm data; differentiating between the respiratory rhythm dataand the cardiac rhythm data by filtering and principal componentanalysis followed by independent component analysis for featurereduction and extraction through conditioning of acquired rhythm data;using fast Fourier transform for frequency analysis of the conditionedrhythm data to determine a power level of respective dominantfrequencies; and correlating the respective dominant frequencies with arespiratory signal and a cardiac signal of the animal.
 11. The method ofclaim 10, further comprising determining when one or more of arespiratory rate and a cardiac rate of the animal exceeds one or more ofa predefined threshold and historical threshold indicative of possibledistress in the animal.
 12. The method of claim 11, further comprisingnotifying one or more remote caretakers of the possible distress in theanimal based on the determining.
 13. A method for mobile equinesurveillance and distress monitoring comprising: monitoring at least oneof the respiratory rate and the heart rate of an animal using UWB-IR;monitoring the temperature of an animal using a thermal infrared sensor;monitoring the behavior of the animal using at least one of anaccelerometer, gyroscope, magnetometer, and barometric pressure sensor;determining the posture and location of the animal using at least one ofa barometric pressure sensor, global positioning system sensor, andWi-Fi triangulation; determining when at least one parameter of therespiratory rate, heart rate, temperature, behavior, and posture of theanimal meets a single threshold value; determining when a combination ofparameters of the respiratory rate, heart rate, temperature, behavior,and posture of the animal meets a composite threshold value indicativeof possible distress in the animal; and activating an escalatingnotification protocol across multiple channels to inform one or moreremote caretakers of possible distress in the animal.
 14. The method ofclaim 13, further comprising detecting one or more biologic functionparameter of the animal.
 15. The method of claim 14, further comprisingmonitoring digestive activity of the animal using a microphone.
 16. Themethod of claim 14, further comprising: detecting one or more novelevents though use of a one-class classifier when one or more detectedbiologic function parameter falls outside one or more of predefinedparameters and historical parameters for the animal; and updating arange of predefined biologic function parameter values andqualifications, single threshold values, and composite threshold valuesin an on-going fashion to conform to detected parameters for the animalover time.
 17. The method of claim 13, further comprising determiningoccurrence of one or more novel events though use of a one-classclassifier when one or more detected parameters fall outside one or moreof predefined parameters and historical parameters for the animal. 18.The method of claim 13, further comprising updating a range ofpredefined parameter values and qualifications, single threshold values,and composite threshold values in an on-going fashion to conform todetected parameters for the animal over time.
 19. The method of claim13, further comprising sending a notification when one or more of theheart rate, respiratory rate, and temperature is outside anadaptively-derived empirical upper limit of normal and lower limit ofnormal for the animal while at rest.
 20. The method of 19, furthercomprising: generating one or more of a first watch notification whenthe heart rate is greater than about 15% above the resting normal (RN)or is greater than about 15% below the RN for a period of time, a secondwarning notification when the heart rate is greater than about 40% abovethe RN or is greater than about 40% below RN for a period of time, and athird alert notification when the heart rate is greater than about 70%above the RN or is greater than about 70% below RN for a period of time;generating one or more of the first watch notification when therespiratory rate is greater than about 35% above the RN or is greaterthan about 35% below RN for a period of time, the second warningnotification when the respiratory rate is greater than about 75% abovethe RN or is greater than about 50% below the RN for a period of time,and the third alert notification when the respiratory rate is greaterthan about 150% above the RN or is greater than about 65% below the RNfor a period of time; and generating one or more of the first watchnotification when the temperature is greater than about 1% above the RNor is greater than about 1% below the RN for a period of time, thesecond warning notification when the temperature is greater than about2% above the RN or is greater than about 1.75% below the RN for a periodof time, and the third alert notification when the temperature isgreater than about 4% above the RN or is greater than about 3.5% belowthe RN for a period of time.